Risk exposure for all kinds of industries occurs in a great variety of aspects, each having their own specific characteristics and complex behavior. The complexity of the behavior of risk exposure driven technical processes often has its background in the interaction with chaotic processes occurring in nature or other artificial environments. Good examples can be found in weather forecast, earthquake and hurricane forecast or controlling of biological processes such as e.g. related to heart diseases or the like. Monitoring, controlling and steering of technical devices or processes interacting with such risk exposure is one of the main challenges of engineering in industry in the 21st century. Dependent or educed systems or processes from products exposed to risks such as e.g. automated pricing tools in insurance technology or forecast systems for natural perils or stock markets, etc. are naturally connected to the same technical problems. Pricing insurance products is additionally difficult because the pricing must be done before the product is sold, but must reflect results that will not be known for some time after the product has been bought and paid for. With tangible products, “the cost of goods sold” is known before the product is sold because the product is developed from raw materials which were acquired before the product was developed. With insurance products, this is not the case. The price of the coverage is set and all those who buy the coverage pay the premium dollars. Subsequently, claims are paid to the unfortunate few who experience a loss. If the amount of claims paid is greater than the amount of premium dollars collected, then the insurance system will make less than their expected profit and may possibly lose money. If the insurance system has been able to predict the amount of claims to be paid and has collected the right amount of premiums, then the system will be profitable.
The price of an insurance product is triggered by the exposure of the insured objects to a specific risk or peril and normally by a set of assumptions related to expected losses, expenses, investments, etc. Generally, the largest amount of money paid out by an insurance system is in the payment of claims for loss. Since the actual amounts will not be known until the future, the insurance system must rely on assumptions about what the losses for which exposure will be. If the actual claims payments are less than or equal to the predicted claims payments, then the product will be profitable. If the actual claims are greater than the predicted claims in the assumptions set in pricing, then the product will not be profitable and the insurance system will lose money. Hence, the ability to set assumptions for the expected losses is critical to the success of the product. The present invention was developed to optimize triggering of liability risk driven exposures in the insurance system technology and to give the technical basics to provide a fully automated pricing device for liability exposure comprising self-adapting and self-optimizing means based upon varying liability risk drivers.
An insurance system must comprise a set of assumptions which reflect the probabilities of occurrence of the loss being insured, the probability of the number of people who will lapse the coverage (that is, stop paying their premiums), and other financial elements such as future developments in expenses, interest rates and taxes. Insurance systems can use historical data on losses to help them to predict what future losses will be. Professionals with experience in mathematics and statistics called actuaries develop tables of losses that incorporate the rate of loss for the group over time into cumulative loss rates. These tables of cumulative loss rates can be used as one of the bases for pricing insurance products.
In pricing a specific product, the system may start with the basic loss tables. Then, based upon judgments concerning the specific nature of the table, the risk to which it is applied, the design of the product, the risk selection techniques applied at the time the policy is issued, and other factors, the insurance system can comprise a set of assumptions for the cumulative loss rates to serve as the foundation for the expected future claims of the product and its risk exposures, respectively. Depending upon the specific insurance product being developed, the historical data and the loss tables do not always correlate well with the specific risks which the policy has to cover. For example, most historical data and/or insurance tables deal with the average probability of loss in an insured set of insured objects. However, some insurance products are directed to subgroups in a set. For example, exposure may drastically vary in these subgroups. For example, insured objects in an urban environment may not show the same liability exposure as such objects in a rural environment, i.e. may be region-dependent. In order to price products for such insured objects, insurance systems must be able to segment the cumulative loss rate from the standard loss tables into cohorts to tease out the loss of those who are objectively less risk exposed within the standard group, and to tune assumptions on these more specific subsets of the population. Segmenting these cumulative loss rates requires that the insurance system has somehow to be able to trigger risk factors for loss which characterize the general insured set of insured objects versus the risk factors which signal the subset with preferred loss. However, most historic data and/or standard loss tables do not take into consideration such separate risk factors. The insurance systems must trigger other sources of data to determine loss rates of specific subsets of insurance objects and/or conditions and the risk factors which are correlated with them. Then, in the process of pricing a product which differentiates price based upon the risk factors, the insurance system must set assumptions as to how these risk factors correlate with the cumulative loss rates in the loss table. Therefore, designing and pricing an insurance product is often an adaptive process which is difficult to achieve by technical means. To arrive at the overall exposure, the insurance system must be able to trigger the appropriate assumptions of loss in which there may be multiple risk factors, each one, individually or in combination with other factors, derived from different simulations, historical data and loss tables.